5 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of TübingenDE5 papers
- Bogolyubov Institute for Theoretical PhysicsUA1 paper
- École PolytechniqueFR1 paper
- Hertie Institute for Clinical Brain ResearchDE1 paper
- Institute of Physics and TechnologyKZ1 paper
- Laboratoire d'Informatique de l'École PolytechniqueFR1 paper
- Moscow Institute of Physics and TechnologyRU1 paper
- National Academy of Sciences of UkraineUA1 paper
- National Research University Higher School of EconomicsRU1 paper
- Taras Shevchenko National University of KyivUA1 paper
- TU Dortmund UniversityDE1 paper
- University of CopenhagenDK1 paper
5 papers
Active galactic nuclei through the prism of galaxy clusters: bounds on axion-like particles
Denys Malyshev, Lidiia Zadorozhna, Yuriy Bidasyuk +2
Hypothetical axion-like particles (ALPs) are of interest because of their potential to act as dark matter or to reveal information about yet undiscovered fundamental constituents o…
Hilbert space methods for approximating multi-output latent variable Gaussian processes
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approxim…
Expansion and ongoing cosmic ray acceleration in HESS J1731-347
Victor Doroshenko, Gerd Pühlhofer, Andrea Santangelo
Diffusive shock acceleration in supernova remnants (SNRs) is considered one of the prime mechanisms for Galactic Cosmic Ray (GCR) acceleration. It is still unclear, however, whethe…
A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images
Mariia Drozdova, Anton Broilovskiy, Andrey Ustyuzhanin +1
Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad…
Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data
Alessandro Salatiello, Martin A. Giese
Recurrent Neural Networks (RNNs) are popular models of brain function. The typical training strategy is to adjust their input-output behavior so that it matches that of the biologi…